diff options
Diffstat (limited to 'numpy/linalg/tests/test_linalg.py')
| -rw-r--r-- | numpy/linalg/tests/test_linalg.py | 117 |
1 files changed, 93 insertions, 24 deletions
diff --git a/numpy/linalg/tests/test_linalg.py b/numpy/linalg/tests/test_linalg.py index 2dc55ab5e..881311c94 100644 --- a/numpy/linalg/tests/test_linalg.py +++ b/numpy/linalg/tests/test_linalg.py @@ -515,30 +515,37 @@ class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase, TestCase): class _TestNorm(TestCase): + dt = None dec = None + def test_empty(self): assert_equal(norm([]), 0.0) assert_equal(norm(array([], dtype=self.dt)), 0.0) assert_equal(norm(atleast_2d(array([], dtype=self.dt))), 0.0) def test_vector(self): - a = [1.0,2.0,3.0,4.0] - b = [-1.0,-2.0,-3.0,-4.0] - c = [-1.0, 2.0,-3.0, 4.0] + a = [1, 2, 3, 4] + b = [-1, -2, -3, -4] + c = [-1, 2, -3, 4] def _test(v): - np.testing.assert_almost_equal(norm(v), 30**0.5, decimal=self.dec) - np.testing.assert_almost_equal(norm(v,inf), 4.0, decimal=self.dec) - np.testing.assert_almost_equal(norm(v,-inf), 1.0, decimal=self.dec) - np.testing.assert_almost_equal(norm(v,1), 10.0, decimal=self.dec) - np.testing.assert_almost_equal(norm(v,-1), 12.0/25, - decimal=self.dec) - np.testing.assert_almost_equal(norm(v,2), 30**0.5, - decimal=self.dec) - np.testing.assert_almost_equal(norm(v,-2), ((205./144)**-0.5), - decimal=self.dec) - np.testing.assert_almost_equal(norm(v,0), 4, decimal=self.dec) + np.testing.assert_almost_equal(norm(v), 30**0.5, + decimal=self.dec) + np.testing.assert_almost_equal(norm(v, inf), 4.0, + decimal=self.dec) + np.testing.assert_almost_equal(norm(v, -inf), 1.0, + decimal=self.dec) + np.testing.assert_almost_equal(norm(v, 1), 10.0, + decimal=self.dec) + np.testing.assert_almost_equal(norm(v, -1), 12.0/25, + decimal=self.dec) + np.testing.assert_almost_equal(norm(v, 2), 30**0.5, + decimal=self.dec) + np.testing.assert_almost_equal(norm(v, -2), ((205./144)**-0.5), + decimal=self.dec) + np.testing.assert_almost_equal(norm(v, 0), 4, + decimal=self.dec) for v in (a, b, c,): _test(v) @@ -548,25 +555,82 @@ class _TestNorm(TestCase): _test(v) def test_matrix(self): - A = matrix([[1.,3.],[5.,7.]], dtype=self.dt) - A = matrix([[1.,3.],[5.,7.]], dtype=self.dt) + A = matrix([[1, 3], [5, 7]], dtype=self.dt) assert_almost_equal(norm(A), 84**0.5) - assert_almost_equal(norm(A,'fro'), 84**0.5) - assert_almost_equal(norm(A,inf), 12.0) - assert_almost_equal(norm(A,-inf), 4.0) - assert_almost_equal(norm(A,1), 10.0) - assert_almost_equal(norm(A,-1), 6.0) - assert_almost_equal(norm(A,2), 9.1231056256176615) - assert_almost_equal(norm(A,-2), 0.87689437438234041) + assert_almost_equal(norm(A, 'fro'), 84**0.5) + assert_almost_equal(norm(A, inf), 12.0) + assert_almost_equal(norm(A, -inf), 4.0) + assert_almost_equal(norm(A, 1), 10.0) + assert_almost_equal(norm(A, -1), 6.0) + assert_almost_equal(norm(A, 2), 9.1231056256176615) + assert_almost_equal(norm(A, -2), 0.87689437438234041) self.assertRaises(ValueError, norm, A, 'nofro') self.assertRaises(ValueError, norm, A, -3) self.assertRaises(ValueError, norm, A, 0) + def test_axis(self): + # Vector norms. + # Compare the use of `axis` with computing the norm of each row + # or column separately. + A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) + for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]: + expected0 = [norm(A[:,k], ord=order) for k in range(A.shape[1])] + assert_almost_equal(norm(A, ord=order, axis=0), expected0) + expected1 = [norm(A[k,:], ord=order) for k in range(A.shape[0])] + assert_almost_equal(norm(A, ord=order, axis=1), expected1) + + # Matrix norms. + B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4) + + for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro']: + assert_almost_equal(norm(A, ord=order), norm(A, ord=order, + axis=(0, 1))) + + n = norm(B, ord=order, axis=(1, 2)) + expected = [norm(B[k], ord=order) for k in range(B.shape[0])] + assert_almost_equal(n, expected) + + n = norm(B, ord=order, axis=(2, 1)) + expected = [norm(B[k].T, ord=order) for k in range(B.shape[0])] + assert_almost_equal(n, expected) + + n = norm(B, ord=order, axis=(0, 2)) + expected = [norm(B[:,k,:], ord=order) for k in range(B.shape[1])] + assert_almost_equal(n, expected) + + n = norm(B, ord=order, axis=(0, 1)) + expected = [norm(B[:,:,k], ord=order) for k in range(B.shape[2])] + assert_almost_equal(n, expected) + + def test_bad_args(self): + # Check that bad arguments raise the appropriate exceptions. + + A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt) + B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4) + + # Using `axis=<integer>` or passing in a 1-D array implies vector + # norms are being computed, so also using `ord='fro'` raises a + # ValueError. + self.assertRaises(ValueError, norm, A, 'fro', 0) + self.assertRaises(ValueError, norm, [3, 4], 'fro', None) + + # Similarly, norm should raise an exception when ord is any finite + # number other than 1, 2, -1 or -2 when computing matrix norms. + for order in [0, 3]: + self.assertRaises(ValueError, norm, A, order, None) + self.assertRaises(ValueError, norm, A, order, (0, 1)) + self.assertRaises(ValueError, norm, B, order, (1, 2)) + + # Invalid axis + self.assertRaises(ValueError, norm, B, None, 3) + self.assertRaises(ValueError, norm, B, None, (2, 3)) + self.assertRaises(ValueError, norm, B, None, (0, 1, 2)) + class TestNormDouble(_TestNorm): dt = np.double - dec= 12 + dec = 12 class TestNormSingle(_TestNorm): @@ -574,6 +638,11 @@ class TestNormSingle(_TestNorm): dec = 6 +class TestNormInt64(_TestNorm): + dt = np.int64 + dec = 12 + + class TestMatrixRank(object): def test_matrix_rank(self): # Full rank matrix |